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Fulltext Long-term exposure to outdoor and household air pollution and blood pressure in the Prospective Urban and Rural Epidemiological (PURE) study

Arku RE, Brauer M, Ahmed SH, AlHabib KF, Avezum Á, Bo J, et al.

Environ Pollut, 2020 Jul;262:114197.
PMID: 32146361 DOI: 10.1016/j.envpol.2020.114197

Exposure to air pollution has been linked to elevated blood pressure (BP) and hypertension, but most research has focused on short-term (hours, days, or months) exposures at relatively low concentrations. We examined the associations between long-term (3-year average) concentrations of outdoor PM2.5 and household air pollution (HAP) from cooking with solid fuels with BP and hypertension in the Prospective Urban and Rural Epidemiology (PURE) study. Outdoor PM2.5 exposures were estimated at year of enrollment for 137,809 adults aged 35-70 years from 640 urban and rural communities in 21 countries using satellite and ground-based methods. Primary use of solid fuel for cooking was used as an indicator of HAP exposure, with analyses restricted to rural participants (n = 43,313) in 27 study centers in 10 countries. BP was measured following a standardized procedure and associations with air pollution examined with mixed-effect regression models, after adjustment for a comprehensive set of potential confounding factors. Baseline outdoor PM2.5 exposure ranged from 3 to 97 μg/m3 across study communities and was associated with an increased odds ratio (OR) of 1.04 (95% CI: 1.01, 1.07) for hypertension, per 10 μg/m3 increase in concentration. This association demonstrated non-linearity and was strongest for the fourth (PM2.5 > 62 μg/m3) compared to the first (PM2.5

Matched MeSH terms: Particulate Matter/analysis
Characterization and source apportionment of particle number concentration at a semi-urban tropical environment

Khan MF, Latif MT, Amil N, Juneng L, Mohamad N, Nadzir MS, et al.

Environ Sci Pollut Res Int, 2015 Sep;22(17):13111-26.
PMID: 25925145 DOI: 10.1007/s11356-015-4541-4

Principal component analysis (PCA) and correlation have been used to study the variability of particle mass and particle number concentrations (PNC) in a tropical semi-urban environment. PNC and mass concentration (diameter in the range of 0.25->32.0 μm) have been measured from 1 February to 26 February 2013 using an in situ Grimm aerosol sampler. We found that the 24-h average total suspended particulates (TSP), particulate matter ≤10 μm (PM10), particulate matter ≤2.5 μm (PM2.5) and particulate matter ≤1 μm (PM1) were 14.37 ± 4.43, 14.11 ± 4.39, 12.53 ± 4.13 and 10.53 ± 3.98 μg m(-3), respectively. PNC in the accumulation mode (<500 nm) was the most abundant (at about 99 %). Five principal components (PCs) resulted from the PCA analysis where PC1 (43.8 % variance) predominates with PNC in the fine and sub-microme tre range. PC2, PC3, PC4 and PC5 explain 16.5, 12.4, 6.0 and 5.6 % of the variance to address the coarse, coarser, accumulation and giant fraction of PNC, respectively. Our particle distribution results show good agreement with the moderate resolution imaging spectroradiometer (MODIS) distribution.

Matched MeSH terms: Particulate Matter/analysis*
Fulltext Towards Integrated Air Pollution Monitoring and Health Impact Assessment Using Federated Learning: A Systematic Review

Neo EX, Hasikin K, Mokhtar MI, Lai KW, Azizan MM, Razak SA, et al.

Front Public Health, 2022;10:851553.
PMID: 35664109 DOI: 10.3389/fpubh.2022.851553

Environmental issues such as environmental pollutions and climate change are the impacts of globalization and become debatable issues among academics and industry key players. One of the environmental issues which is air pollution has been catching attention among industrialists, researchers, and communities around the world. However, it has always neglected until the impacts on human health become worse, and at times, irreversible. Human exposure to air pollutant such as particulate matters, sulfur dioxide, ozone and carbon monoxide contributed to adverse health hazards which result in respiratory diseases, cardiorespiratory diseases, cancers, and worst, can lead to death. This has led to a spike increase of hospitalization and emergency department visits especially at areas with worse pollution cases that seriously impacting human life and health. To address this alarming issue, a predictive model of air pollution is crucial in assessing the impacts of health due to air pollution. It is also critical in predicting the air quality index when assessing the risk contributed by air pollutant exposure. Hence, this systemic review explores the existing studies on anticipating air quality impact to human health using the advancement of Artificial Intelligence (AI). From the extensive review, we highlighted research gaps in this field that are worth to inquire. Our study proposes to develop an AI-based integrated environmental and health impact assessment system using federated learning. This is specifically aims to identify the association of health impact and pollution based on socio-economic activities and predict the Air Quality Index (AQI) for impact assessment. The output of the system will be utilized for hospitals and healthcare services management and planning. The proposed solution is expected to accommodate the needs of the critical and prioritization of sensitive group of publics during pollution seasons. Our finding will bring positive impacts to the society in terms of improved healthcare services quality, environmental and health sustainability. The findings are beneficial to local authorities either in healthcare or environmental monitoring institutions especially in the developing countries.

Matched MeSH terms: Particulate Matter/analysis
Short communication: Diagnosis of lung cancer increases during the annual southeast Asian haze periods

Hassan A, Latif MT, Soo CI, Faisal AH, Roslina AM, Andrea YLB, et al.

Lung Cancer, 2017 11;113:1-3.
PMID: 29110834 DOI: 10.1016/j.lungcan.2017.08.025

There have been few but timely studies examining the role of air pollution in lung cancer and survival. The Southeast Asia haze is a geopolitical problem that has occurred annually since 1997 in countries such as Malaysia, Singapore and Indonesia. To date, there has been no study examining the impact of the annual haze in the presentation of lung cancer. Data on all lung cancers and respiratory admissions to Universiti Kebangsaan Malaysia Medical Centre (UKMMC) from 1st January 2010 to 31th October 2015 were retrospectively collected and categorized as presentation during the haze and non-haze periods defined by the Department of Environment Malaysia. We report a lung cancer incidence rate per week of 4.5 cases during the haze compared to 1.8 cases during the non-haze period (p<0.01). The median survival for subjects presenting during the haze was 5.2 months compared to 8.1 months for the non-haze period (p<0.05). The majority of subjects diagnosed during the haze period initially presented with acute symptoms. Although this study could not suggest a cause and effect relationship of the annual haze with the incidence of lung cancer, this is the first study reporting a local air pollution-related modifiable determinant contributing to the increase in presentation of lung cancer in Southeast Asia.

Matched MeSH terms: Particulate Matter/analysis
Fulltext Metagenomic characterization of indoor dust fungal associated with allergy and lung inflammation among school children

Isa KNM, Jalaludin J, Elias SM, Than LTL, Jabbar MA, Saudi ASM, et al.

Ecotoxicol Environ Saf, 2021 Sep 15;221:112430.
PMID: 34147866 DOI: 10.1016/j.ecoenv.2021.112430

The exposure of school children to indoor air pollutants has increased allergy and respiratory diseases. The objective of this study were to determine the toxicodynamic interaction of indoor pollutants exposure, biological and chemical with expression of adhesion molecules on eosinophil and neutrophil. A self-administered questionnaire, allergy skin test, and fractional exhaled nitric oxide (FeNO) analyser were used to collect information on health status, sensitization to allergens and respiratory inflammation, respectively among school children at age of 14 years. The sputum induced were analysed to determine the expression of CD11b, CD35, CD63 and CD66b on eosinophil and neutrophil by using flow cytometry technique. The particulate matter (PM2.5 and PM10), NO2, CO2, and formaldehyde, temperature, and relative humidity were measured inside the classrooms. The fungal DNA were extracted from settled dust collected from classrooms and evaluated using metagenomic techniques. We applied chemometric and regression in statistical analysis. A total of 1869 unique of operational taxonomic units (OTUs) of fungi were identified with dominated at genus level by Aspergillus (15.8%), Verrucoconiothyrium (5.5%), and Ganoderma (4.6%). Chemometric and regression results revealed that relative abundance of T. asahii were associated with down regulation of CD66b expressed on eosinophil, and elevation of FeNO levels in predicting asthmatic children with model accuracy of 63.6%. Meanwhile, upregulation of CD11b expressed on eosinophil were associated with relative abundance of A. clavatus and regulated by PM2.5. There were significant association of P. bandonii with upregulation of CD63 expressed on neutrophil and exposure to NO2. Our findings indicate that exposure to PM2.5, NO2, T. asahii, P.bandonii and A.clavatus are likely interrelated with upregulation of activation and degranulation markers on both eosinophil and neutrophil.

Matched MeSH terms: Particulate Matter/analysis
Evaluation of the Relationship between Fractional Exhaled Nitric Oxide (FeNO) with Indoor PM10, PM2.5 and NO2 in Suburban and Urban Schools

Mohd Isa KN, Jalaludin J, Mohd Elias S, Mohamed N, Hashim JH, Hashim Z

Int J Environ Res Public Health, 2022 Apr 11;19(8).
PMID: 35457448 DOI: 10.3390/ijerph19084580

Numerous epidemiological studies have evaluated the association of fractional exhaled nitric oxide (FeNO) and indoor air pollutants, but limited information available of the risks between schools located in suburban and urban areas. We therefore investigated the association of FeNO levels with indoor particulate matter (PM10 and PM2.5), and nitrogen dioxide (NO2) exposure in suburban and urban school areas. A comparative cross-sectional study was undertaken among secondary school students in eight schools located in the suburban and urban areas in the district of Hulu Langat, Selangor, Malaysia. A total of 470 school children (aged 14 years old) were randomly selected, their FeNO levels were measured, and allergic skin prick tests were conducted. The PM2.5, PM10, NO2, and carbon dioxide (CO2), temperature, and relative humidity were measured inside the classrooms. We found that the median of FeNO in the school children from urban areas (22.0 ppb, IQR = 32.0) were slightly higher as compared to the suburban group (19.5 ppb, IQR = 24.0). After adjustment of potential confounders, the two-level hierarchical multiple logistic regression models showed that the concentrations of PM2.5 were significantly associated with elevated of FeNO (>20 ppb) in school children from suburban (OR = 1.42, 95% CI = 1.17−1.72) and urban (OR = 1.30, 95% CI = 1.10−1.91) areas. Despite the concentrations of NO2 being below the local and international recommendation guidelines, NO2 was found to be significantly associated with the elevated FeNO levels among school children from suburban areas (OR = 1.11, 95% CI = 1.06−1.17). The findings of this study support the evidence of indoor pollutants in the school micro-environment associated with FeNO levels among school children from suburban and urban areas.

Matched MeSH terms: Particulate Matter/analysis
Impact of Climate Change on Air Quality and Public Health in Urban Areas

Hassan NA, Hashim Z, Hashim JH

Asia Pac J Public Health, 2016 Mar;28(2 Suppl):38S-48S.
PMID: 26141092 DOI: 10.1177/1010539515592951

This review discusses how climate undergo changes and the effect of climate change on air quality as well as public health. It also covers the inter relationship between climate and air quality. The air quality discussed here are in relation to the 5 criteria pollutants; ozone (O3), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM). Urban air pollution is the main concern due to higher anthropogenic activities in urban areas. The implications on health are also discussed. Mitigating measures are presented with the final conclusion.

Matched MeSH terms: Particulate Matter/analysis
Indoor generated PM2.5 compositions and volatile organic compounds: Potential sources and health risk implications

Idris SA', Hanafiah MM, Khan MF, Hamid HHA

Chemosphere, 2020 Sep;255:126932.
PMID: 32402880 DOI: 10.1016/j.chemosphere.2020.126932

The aim of the present study was to investigate the potential sources of heavy metals in fine air particles (PM2.5) and benzene, toluene, ethylbenzene, and isomeric xylenes (BTEX) in gas phase indoor air. PM2.5 samples were collected using a low volume sampler. BTEX samples were collected using passive sampling onto sorbent tubes and analyzed using gas chromatography-mass spectrometry (GC-MS). For the lower and upper floors of the evaluated building, the concentrations of PM2.5 were 96.4 ± 2.70 μg/m3 and 80.2 ± 3.11 μg/m3, respectively. The compositions of heavy metals in PM2.5 were predominated by iron (Fe), zinc (Zn), and aluminum (Al) with concentration of 500 ± 50.07 ng/m3, 466 ± 77.38 ng/m3, and 422 ± 147.38 ng/m3. A principal component analysis (PCA) showed that the main sources of BTEX were originated from vehicle emissions and exacerbate because of temperature variations. Hazard quotient results for BTEX showed that the compounds were below acceptable limits and thus did not possess potential carcinogenic risks. However, a measured output of lifetime cancer probability revealed that benzene and ethylbenzene posed definite carcinogenic risks. Pollutants that originated from heavy traffic next to the sampling site contributed to the indoor pollution.

Matched MeSH terms: Particulate Matter/analysis*
Evaluation of the potential bioaccumulation ability of the blood cockle (Anadara granosa L.) for assessment of environmental matrices of mudflats

Mirsadeghi SA, Zakaria MP, Yap CK, Gobas F

Sci Total Environ, 2013 Jun 1;454-455:584-97.
PMID: 23583984 DOI: 10.1016/j.scitotenv.2013.03.001

The spatial distribution of 19 polycyclic aromatic hydrocarbons (tPAHs) was quantified in aquacultures located in intertidal mudflats of the west coast of Peninsular Malaysia in order to investigate bioaccumulation of PAH in blood cockles, Anadara granosa (A. granosa). Fifty-four samples from environmental matrices and A. granosa were collected. The sampling locations were representative of a remote area as well as PAH-polluted areas. The relationship of increased background levels of PAH to anthropogenic PAH sources in the environment and their effects on bioaccumulation levels of A. granosa are investigated in this study. The levels of PAH in the most polluted station were found to be up to ten-fold higher than in remote areas in blood cockle. These high concentrations of PAHs reflected background contamination, which originates from distant airborne and waterborne transportation of contaminated particles. The fraction and source identification of PAHs, based on fate and transport considerations, showed a mix of petrogenic and pyrogenic sources. The relative biota-sediment accumulation factors (RBSAF), relative bioaccumulation factors from filtered water (RBAFw), and from suspended particulate matter (SPM) (RBAFSP) showed higher bioaccumulations of the lower molecular weight of PAHs (LMWs) in all stations, except Kuala Juru, which showed higher bioaccumulation of the higher molecular weight of PAHs (HMWs). Calculations of bioaccumulation factors showed that blood cockle can accumulate PAHs from sediment as well as water samples, based on the physico-chemical characteristics of habitat and behaviour of blood cockles. Correlations among concentrations of PAHs in water, SPM, sediment and A. granosa at the same sites were also found. Identification of PAH levels in different matrices showed that A. granosa can be used as a good biomonitor for LMW of PAHs and tPAHs in mudflats. Considering the toxicity and carcinogenicity of PAHs, the bioaccumulation by blood cockles are a potential hazard for both blood cockles and their consumers.

Matched MeSH terms: Particulate Matter/analysis
Fulltext Indoor Particulate Matters, Microbial Count Assessments, and Wheezing Symptoms among Toddlers in Urban Day Care Centers in the District of Seremban, Malaysia

Khamal R, Isa ZM, Sutan R, Noraini NMR, Ghazi HF

Ann Glob Health, 2019 01 22;85(1).
PMID: 30741516 DOI: 10.5334/aogh.2425

INTRODUCTION: Indoor air quality in day care centers (DCCs) is an emerging research topic nowadays. Indoor air pollutants such as particulate matter (PM) and microbes have been linked to respiratory health effects in children, particularly asthma-related symptoms such as night coughs and wheezing due to early exposure to indoor air contaminants.
OBJECTIVE: The aim of this study was to determine the association between wheezing symptoms among toddlers attending DCCs and indoor particulate matter, PM10, PM2.5, and microbial count level in urban DCCs in the District of Seremban, Malaysia.
METHODS: Data collection was carried out at 10 DCCs located in the urban area of Seremban. Modified validated questionnaires were distributed to parents to obtain their children's health symptoms. The parameters measured were indoor PM2.5, PM10, carbon monoxide, total bacteria count, total fungus count, temperature, air velocity, and relative humidity using the National Institute for Occupational Safety and Health analytical method.
RESULTS: All 10 DCCs investigated had at least one indoor air quality parameter exceeding the acceptable level of standard guidelines. The prevalence of toddlers having wheezing symptoms was 18.9%. There was a significant different in mean concentration of PM2.5 and total bacteria count between those with and those without wheezing symptoms (P = 0.02, P = 0.006).
CONCLUSIONS: Urban DCCs are exposed to many air pollutants that may enter their buildings from various adjacent sources. The particle concentrations and presence of microbes in DCCs might increase the risk of exposed children for respiratory diseases, particularly asthma, in their later life.

Matched MeSH terms: Particulate Matter/analysis*
Water quality evaluation system to assess the status and the suitability of the Citarum river water to different uses

Fulazzaky MA

Environ Monit Assess, 2010 Sep;168(1-4):669-84.
PMID: 19728125 DOI: 10.1007/s10661-009-1142-z

Water quality degradation in the Citarum river will increase from the year to year due to increasing pollutant loads when released particularly from Bandung region of the upstream areas into the river without treatment. This will be facing the problems on water quality status to use for multi-purposes in the downstream areas. The water quality evaluation system is used to evaluate the available water condition that distinguishes into two categories, i.e., the water quality index (WQI) and water quality aptitude (WQA). The assessment of water quality for the Citarum river from 10 selected stations was found that the WQI situates in the bad category generally and the WQA ranges from the suitable quality for agriculture and livestock watering uses to the unsuitable for biological potential function, drinking water production, and leisure activities and sports in the upstream areas of Saguling dam generally.

Matched MeSH terms: Particulate Matter/analysis
Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia

Syed Abdul Mutalib SN, Juahir H, Azid A, Mohd Sharif S, Latif MT, Aris AZ, et al.

Environ Sci Process Impacts, 2013 Sep;15(9):1717-28.
PMID: 23831918 DOI: 10.1039/c3em00161j

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.

Matched MeSH terms: Particulate Matter/analysis*
A review of personal exposure studies in selected Asian countries' public transport microenvironments: lessons learned and future directions

Otuyo MK, Nadzir MSM, Latif MT, Din SAM

Environ Sci Pollut Res Int, 2023 Dec;30(58):121306-121337.
PMID: 37993649 DOI: 10.1007/s11356-023-30923-9

This comprehensive paper conducts an in-depth review of personal exposure and air pollutant levels within the microenvironments of Asian city transportation. Our methodology involved a systematic analysis of an extensive body of literature from diverse sources, encompassing a substantial quantity of studies conducted across multiple Asian cities. The investigation scrutinizes exposure to various pollutants, including particulate matters (PM10, PM2.5, and PM1), carbon dioxide (CO2), formaldehyde (CH2O), and total volatile organic compounds (TVOC), during transportation modes such as car travel, bus commuting, walking, and train rides. Notably, our review reveals a predominant focus on PM2.5, followed by PM10, PM1, CO2, and TVOC, with limited attention given to CH2O exposure. Across the spectrum of Asian cities and transportation modes, exposure concentrations exhibited considerable variability, a phenomenon attributed to a multitude of factors. Primary sources of exposure encompass motor vehicle emissions, traffic dynamics, road dust, and open bus doors. Furthermore, our findings illuminate the influence of external environments, particularly in proximity to train stations, on pollutant levels inside trains. Crucial factors affecting exposure encompass ventilation conditions, travel-specific variables, seat locations, vehicle types, and meteorological influences. The culmination of this rigorous review underscores the need for standardized measurements, enhanced ventilation systems, air filtration mechanisms, the adoption of clean energy sources, and comprehensive public education initiatives aimed at reducing pollutant exposure within city transportation microenvironments. Importantly, our study contributes to the growing body of knowledge surrounding this subject, offering valuable insights for policymakers and researchers dedicated to advancing air quality standards and safeguarding public health.

Matched MeSH terms: Particulate Matter/analysis
Fulltext PM2.5 forecasting for an urban area based on deep learning and decomposition method

Zaini N, Ean LW, Ahmed AN, Abdul Malek M, Chow MF

Sci Rep, 2022 Oct 20;12(1):17565.
PMID: 36266317 DOI: 10.1038/s41598-022-21769-1

Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.

Matched MeSH terms: Particulate Matter/analysis
Spatial distribution of fine and coarse particulate matter during a southwest monsoon in Peninsular Malaysia

Othman M, Latif MT, Jamhari AA, Abd Hamid HH, Uning R, Khan MF, et al.

Chemosphere, 2021 Jan;262:127767.
PMID: 32763576 DOI: 10.1016/j.chemosphere.2020.127767

This study aimed to determine the spatial distribution of PM2.5 and PM10 collected in four regions (North, Central, South and East Coast) of Peninsular Malaysia during the southwest monsoon. Concurrent measurements of PM2.5 and PM10 were performed using a high volume sampler (HVS) for 24 h (August to September 2018) collecting a total of 104 samples. All samples were then analysed for water soluble inorganic ions (WSII) using ion chromatography, trace metals using inductively coupled plasma-mass spectroscopy (ICP-MS) and polycyclic aromatic hydrocarbon (PAHs) using gas chromatography-mass spectroscopy (GC-MS). The results showed that the highest average PM2.5 concentration during the sampling campaign was in the North region (33.2 ± 5.3 μg m-3) while for PM10 the highest was in the Central region (38.6 ± 7.70 μg m-3). WSII recorded contributions of 22% for PM2.5 and 20% for PM10 mass, with SO42- the most abundant species with average concentrations of 1.83 ± 0.42 μg m-3 (PM2.5) and 2.19 ± 0.27 μg m-3 (PM10). Using a Positive Matrix Factorization (PMF) model, soil fertilizer (23%) was identified as the major source of PM2.5 while industrial activity (25%) was identified as the major source of PM10. Overall, the studied metals had hazard quotients (HQ) value of <1 indicating a very low risk of non-carcinogenic elements while the highest excess lifetime cancer risk (ELCR) was recorded for Cr VI in the South region with values of 8.4E-06 (PM2.5) and 6.6E-05 (PM10). The incremental lifetime cancer risk (ILCR) calculated from the PAH concentrations was within the acceptable range for all regions.

Matched MeSH terms: Particulate Matter/analysis*
Fulltext DNA methylation and exposure to ambient air pollution in two prospective cohorts

Plusquin M, Guida F, Polidoro S, Vermeulen R, Raaschou-Nielsen O, Campanella G, et al.

Environ Int, 2017 11;108:127-136.
PMID: 28843141 DOI: 10.1016/j.envint.2017.08.006

Long-term exposure to air pollution has been associated with several adverse health effects including cardiovascular, respiratory diseases and cancers. However, underlying molecular alterations remain to be further investigated. The aim of this study is to investigate the effects of long-term exposure to air pollutants on (a) average DNA methylation at functional regions and, (b) individual differentially methylated CpG sites. An assumption is that omic measurements, including the methylome, are more sensitive to low doses than hard health outcomes. This study included blood-derived DNA methylation (Illumina-HM450 methylation) for 454 Italian and 159 Dutch participants from the European Prospective Investigation into Cancer and Nutrition (EPIC). Long-term air pollution exposure levels, including NO2, NOx, PM2.5, PMcoarse, PM10, PM2.5 absorbance (soot) were estimated using models developed within the ESCAPE project, and back-extrapolated to the time of sampling when possible. We meta-analysed the associations between the air pollutants and global DNA methylation, methylation in functional regions and epigenome-wide methylation. CpG sites found differentially methylated with air pollution were further investigated for functional interpretation in an independent population (EnviroGenoMarkers project), where (N=613) participants had both methylation and gene expression data available. Exposure to NO2 was associated with a significant global somatic hypomethylation (p-value=0.014). Hypomethylation of CpG island's shores and shelves and gene bodies was significantly associated with higher exposures to NO2 and NOx. Meta-analysing the epigenome-wide findings of the 2 cohorts did not show genome-wide significant associations at single CpG site level. However, several significant CpG were found if the analyses were separated by countries. By regressing gene expression levels against methylation levels of the exposure-related CpG sites, we identified several significant CpG-transcript pairs and highlighted 5 enriched pathways for NO2 and 9 for NOx mainly related to the immune system and its regulation. Our findings support results on global hypomethylation associated with air pollution, and suggest that the shores and shelves of CpG islands and gene bodies are mostly affected by higher exposure to NO2 and NOx. Functional differences in the immune system were suggested by transcriptome analyses.

Matched MeSH terms: Particulate Matter/analysis
Spatiotemporal analysis of ground and satellite-based aerosol for air quality assessment in the Southeast Asia region

Nguyen TTN, Pham HV, Lasko K, Bui MT, Laffly D, Jourdan A, et al.

Environ Pollut, 2019 Dec;255(Pt 1):113106.
PMID: 31541826 DOI: 10.1016/j.envpol.2019.113106

Satellite observations for regional air quality assessment rely on comprehensive spatial coverage, and daily monitoring with reliable, cloud-free data quality. We investigated spatiotemporal variation and data quality of two global satellite Aerosol Optical Depth (AOD) products derived from MODIS and VIIRS imagery. AOD is considered an essential atmospheric parameter strongly related to ground Particulate Matter (PM) in Southeast Asia (SEA). We analyze seasonal variation, urban/rural area influence, and biomass burning effects on atmospheric pollution. Validation indicated a strong relationship between AERONET ground AOD and both MODIS AOD (R2 = 0.81) and VIIRS AOD (R2 = 0.68). The monthly variation of satellite AOD and AERONET AOD reflects two seasonal trends of air quality separately for mainland countries including Myanmar, Laos, Cambodia, Thailand, Vietnam, and Taiwan, Hong Kong, and for maritime countries consisting of Indonesia, Philippines, Malaysia, Brunei, Singapore, and Timor Leste. The mainland SEA has a pattern of monthly AOD variation in which AODs peak in March/April, decreasing during wet season from May-September, and increasing to the second peak in October. However, in maritime SEA, AOD concentration peaks in October. The three countries with the highest annual satellite AODs are Singapore, Hong Kong, and Vietnam. High urban population proportions in Singapore (40.7%) and Hong Kong (21.6%) were associated with high AOD concentrations as expected. AOD values in SEA urban areas were a factor of 1.4 higher than in rural areas, with respective averages of 0.477 and 0.336. The AOD values varied proportionately to the frequency of biomass burning in which both active fires and AOD peak in March/April and September/October. Peak AOD in September/October in some countries could be related to pollutant transport of Indonesia forest fires. This study analyzed satellite aerosol product quality in relation to AERONET in SEA countries and highlighted framework of air quality assessment over a large, complicated region.

Matched MeSH terms: Particulate Matter/analysis*
Fulltext Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Masood A, Hameed MM, Srivastava A, Pham QB, Ahmad K, Razali SFM, et al.

Sci Rep, 2023 Nov 29;13(1):21057.
PMID: 38030733 DOI: 10.1038/s41598-023-47492-z

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.

Matched MeSH terms: Particulate Matter/analysis
GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms

Tella A, Balogun AL

Environ Sci Pollut Res Int, 2022 Dec;29(57):86109-86125.
PMID: 34533750 DOI: 10.1007/s11356-021-16150-0

Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.

Matched MeSH terms: Particulate Matter/analysis
Fulltext Chronic airflow obstruction and ambient particulate air pollution

Amaral AFS, Burney PGJ, Patel J, Minelli C, Mejza F, Mannino DM, et al.

Thorax, 2021 12;76(12):1236-1241.
PMID: 33975927 DOI: 10.1136/thoraxjnl-2020-216223

Smoking is the most well-established cause of chronic airflow obstruction (CAO) but particulate air pollution and poverty have also been implicated. We regressed sex-specific prevalence of CAO from 41 Burden of Obstructive Lung Disease study sites against smoking prevalence from the same study, the gross national income per capita and the local annual mean level of ambient particulate matter (PM2.5) using negative binomial regression. The prevalence of CAO was not independently associated with PM2.5 but was strongly associated with smoking and was also associated with poverty. Strengthening tobacco control and improved understanding of the link between CAO and poverty should be prioritised.

Matched MeSH terms: Particulate Matter/analysis

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